Environmental recognition and a localization are important issues for an outdoor mobile robot which performs specialized duties, such as patrolling or servicing in a road. The outdoor mobile robot performing said duties repeatedly drives the same route based on map information provided in advance. The environmental recognition and the localization of the mobile robot are important for a safe navigation of the outdoor mobile robot based on the map.
The outdoor environments typically have two following features. One is that surrounding topography is not standardized. The other is that there are many changes by weather or seasons. Therefore, various researches have proceeded to overcome this environmental uncertainty.
For example, “DARPA Challenge” which is presented in a paper of Sebastian Thrun, et al., “Stanley: The Robot That Won the DARPA Grand Challenge” (Journal of Field Robotics, vol. 23 no. 9, pp. 661-692, 2006) and a paper of Martin Buehler, Karl lagnemma, Sanjiv Singh (Eds.), “The DARPA Urban Challenge: Autonomous Vehicles in City Traffic” (Springer, 2010.) is a successful case of an autonomous navigation of the mobile robot in outdoor environments.
The DARPA Challenge suggests various localization techniques and navigation strategies to maneuver successfully in spite of the uncertainty in the outdoor environments. However, the DARPA Challenge has certain disadvantages. First, many pieces of costly equipment are required. Second, complex system configurations are required.
For the navigation of the mobile robots in outdoor environments, the localization is generally conducted by a combination of information from multiple sensors. Among these sensors, the combination of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) has been widely used. A paper of Solovev, A., “Tight Coupling of GPS, Laser Scanner, and Inertial Measurements for Navigation in urban Environments” (Position, Location and Navigation Symposium, 2008.) and a paper of S. Panzieri, F. Pascucci, and G. Ulivi “An outdoor navigation system using GPS and inertial platform” (IEEE/ASME Trans. Mechatronics, vol. 7, no. 2, pp. 134-142, June, 2002.) are typical cases using the combination of GPS and IMU.
However, GPS measurements are degraded in urban environments surrounded by tall buildings because of multipath errors and blocked satellite in view. This results in imprecise measurements.
To solve the abovementioned GPS errors in urban environments, methods to be aware of the driving environment of the mobile robots and to use it to the localization are presented in many paper, such as a paper of M Joerger and B Pervan, “Range-Domain Integration of GPS and Laser-scanner Measurements for Outdoor Navigation” (Proceedings of the ION GNSS, 2006.).
Among the methods, the technique with a vision sensor has been widely used in recent years. In a paper of Georgiev, A. and Allen, P. K., “Localization Methods for a Mobile Robot in Urban Environments” (IEEE Transactions on Robotics, Vol. 20, Issue 5, 2004.), images of buildings are extracted using a camera, and the position of the mobile robot is corrected by matching a pre-registered map.
However, the vision sensor which is suggested in the paper has some disadvantages. It is hard to operate at night and it is strongly influenced by weather changes because it is not strong in low illumination.
Therefore, Laser Range Finder (LRF) is used in the environmental recognition technique, which is strong in low illumination of driving environments and weather change. In a paper of Daniel Maier and Alexander Kleiner, “Improved GPS Sensor Model for Mobile Robots in Urban Terrain” (IEEE International Conference on Robotics and Automation, pp. 4385-4390, May, 2010.), an awareness of a 3D environment using a LRF is conducted to consider obscured GPS satellites due to tall buildings. However, the localization is conducted with the environmental recognition because only a small amount of geometric information exists in comparison with the wide area.
Generally, the urban road environment is paved and curbs are the boundaries of the roads. These environments are defined as a semi-structured road environment. In the semi-structured road environment, there is little change of the road shape.
Therefore, it is easy to extract the road features, especially the curb, using geometric features. For this reason, the curb has been widely used for navigation strategies and localization techniques in road environments. In a paper of Wijesoma W. S., Kodagoda K. R. S., and Balasuriya A. P., “Road-Boundary Detection and Tracking Using Ladar Sensing” (IEEE Trans. Robot. Automat. vol. 20, pp. 456-464, June 2004.), a technique to extract the curb by using the LRF in the road environment is suggested. In a paper of M. Jabbour and P. Bonnifait, “Global localization robust to gps outages using a vertical radar” (9th International Conference on Control, Automation, Robotics and Vision, vol. 1, no. 5, pp. 1013-1018, 2006.), for localization of vehicle, one side of the curb was extracted using a vertical radar. However, this technique reduces a lateral error of the vehicle through map matching of the extracted curb point, but does not correct the orientation of the mobile robot.